SoroushVahidi/scidocs huggingface dataset:
Ecosystem or Echo-System? Exploring Content Sharing across Alternative Media Domains
(2018)

This research examines the competing narratives about the role and function of Syria Civil Defence, a volunteer humanitarian organization popularly known as the White Helmets, working in war-torn Syria. Using a mixed-method approach based on seed data collected from Twitter, and then extending out to the websites cited in that data, we examine content sharing practices across distinct media domains that functioned to construct, shape, and propagate these narratives. We articulate a predominantly alternative media “echo-system” of websites that repeatedly share content about the White Helmets. Among other findings, our work reveals a small set of websites and authors generating content that is spread across diverse sites, drawing audiences from distinct communities into a shared narrative. This analysis also reveals the integration of governmentfunded media and geopolitical think tanks as source content for anti-White Helmets narratives. More broadly, the analysis demonstrates the role of alternative newswire-like services in providing content for alternative media websites. Though additional work is needed to understand these patterns over time and across topics, this paper provides insight into the dynamics of this multi-layered media ecosystem.
Fake News Detection on Social Media using Geometric Deep Learning
(2019)

Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access, and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to ‘fake news’, intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or ‘common sense’, which current natural language processing algorithms are still missing. Recent studies have empirically shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper, we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical convolutional neural networks to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches. ar X iv :1 90 2. 06 67 3v 1 [ cs .S I] 1 0 Fe b 20 19
Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems
(2015)

Big deep neural network (DNN) models trained on large amounts of data have recently achieved the best accuracy on hard tasks, such as image and speech recognition. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. To enable training of extremely large DNNs, models are partitioned across machines. To expedite training on very large data sets, multiple model replicas are trained in parallel on different subsets of the training examples with a global parameter server maintaining shared weights across these replicas. The correct choice for model and data partitioning and overall system provisioning is highly dependent on the DNN and distributed system hardware characteristics. These decisions currently require significant domain expertise and time consuming empirical state space exploration.
 This paper develops performance models that quantify the impact of these partitioning and provisioning decisions on overall distributed system performance and scalability. Also, we use these performance models to build a scalability optimizer that efficiently determines the optimal system configuration that minimizes DNN training time. We evaluate our performance models and scalability optimizer using a state-of-the-art distributed DNN training framework on two benchmark applications. The results show our performance models estimate DNN training time with high estimation accuracy and our scalability optimizer correctly chooses the best configurations, minimizing the training time of distributed DNNs.
Variance Reduction for Stochastic Gradient Optimization
(2013)

Stochastic gradient optimization is a class of widely used algorithms for training machine learning models. To optimize an objective, it uses the noisy gradient computed from the random data samples instead of the true gradient computed from the entire dataset. However, when the variance of the noisy gradient is large, the algorithm might spend much time bouncing around, leading to slower convergence and worse performance. In this paper, we develop a general approach of using control variate for variance reduction in stochastic gradient. Data statistics such as low-order moments (pre-computed or estimated online) is used to form the control variate. We demonstrate how to construct the control variate for two practical problems using stochastic gradient optimization. One is convex—the MAP estimation for logistic regression, and the other is non-convex—stochastic variational inference for latent Dirichlet allocation. On both problems, our approach shows faster convergence and better performance than the classical approach.
Advances in Variational Inference
(2018)

Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.
Optimized rate matching architecture for a LTE-Advanced FPGA-based PHY
(2013)

In this paper we present an optimized rate matching architecture for a LTE-Advanced FPGA-based physical layer. Since LTE-Advanced can reach up to rates of 1 Gbps in downlink, and since rate matching is in that critical path, it is very important that the design of the hardware architecture be efficient enough to allow this high data rate with little resources as possible. If not well planned, implementations on FPGAs can be quite challenging, limiting the choices of speed grades and FPGAs sizes capable of supporting such requirements. We propose efficient hardware architecture for the LTE-Advanced rate matching generic procedure; occupying only 218 slices and 9 block RAMs and performing in frequencies greater than 400 MHz in a FPGA-based solution.
Linear model predictive control of the locomotion of Pepper, a humanoid robot with omnidirectional wheels
(2014)

The goal of this paper is to present a new real-time controller based on linear model predictive control for an omnidirectionnal wheeled humanoid robot. It is able to control both the mobile base of the robot and its body, while taking into account dynamical constraints. It makes it possible to have high velocity and acceleration motions by predicting the dynamic behavior of the robot in the future. Experimental results are proposed on the robot Pepper made by Aldebaran, showing good performance in terms of robustness and tracking control, efficiently managing kinematic and dynamical constraints.
CLASSIFICATION USING NAÏVE BAYES , VSM AND POS TAGGER
(2017)

This research focuses on Text Classification. Text classification is the task of automatically sorting a set of documents into categories from a predefined set. The domain of this research is the combination of information ret rieval (IR) technology, Data mining and machine learning (ML) technology. This research will outline the fundamental traits of the technologies involved. This research uses three text classification algorithms (Naive Bayes, VSM for text classificatio n and the new technique -Use of Stanford Tagger for text classific ation) to classify documents into different categories, which is trained on two different datasets (20 Newsgroups and New news dataset for five categories).In regards to the above classifica tion strategies, Naïve Bayes is potentially good at serving as a tex classification model due to its simplicity.
Handling non-functional requirements for big data and IOT projects in Scrum
(2017)

Agile methodologies are gaining popularity at a lightning pace in the software industry. Prominent agile methodologies, such as Scrum and Extreme Programming (XP), help quickly deliver a system that meets Functional Requirements (FRs), while adapting to the changes in the customer requirements and feedback. However, Non-Functional Requirements (NFRs) by and large have been either mostly ignored or introduced and retrofit late in the software development cycle, oftentimes leading to project failures. With the recent, increasing shifts towards cloud and emphasis in big data in the software industry, NFRs, such as security and performance, have become more critical than ever before. This research proposes a novel approach to handling security and performance NFRs for projects involving big data and cloud, using Scrum. An industrial case study, conducted over a span of 9 months, shows that the approach helps deal with security and performance requirements individually as well as conflicts between them in an agile methodology.
An Active Testing Model for Tracking Roads in Satellite Images
(1996)

We present a new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy (\active testing") for tracking 1D structures and other recognition tasks in computer vision. Our approach is related to recent work in active vision on \where to look next" and motivated by the \divide-and-conquer" strategy of parlor games such as \Twenty Questions." We choose \tests" (matched lters for short road segments) one at a time in order to remove as much uncertainty as possible about the \true hypothesis" (road position) given the results of the previous tests. The tests are chosen on-line based on a statistical model for the joint distribution of tests and hypotheses. The problem of minimizing uncertainty (measured by entropy) is formulated in simple and explicit analytical terms. To execute this entropy testing rule we then alternate between data collection and optimization: at each iteration new image data are examined and a new entropy minimization problem is solved (exactly), resulting in a new image location to inspect, and so forth. We report experiments using panchromatic SPOT satellite imagery with a ground resolution of ten meters: given a starting point and starting direction, we are able to rapidly track highways in southern France over distances on the order of one hundred kilometers without manual intervention. Une m ethode active pour le suivi de routes dans des images satellitaires R esum e : Nous pr esentons une approche originale pour suivre automatiquement une route dans une image de la Terre acquise par satellite. Ce faisant, nous illustrons une m ethodologie plus g en erale pour le probl eme du suivi de structures lin eiques et autres dans des images. Notre approche est li ee a des travaux r ecents en vision active concernant les strat egies de perception ainsi qu'' a la m ethode algorithmique "diviser pour r egner" dans le jeu des vingt questions. Nous choisissons des questions (d etecteur local pour la pr esence d'une route) s equentiel-lement, de mani ere a r eduire le plus possible l'incertitude sur l'inconnue (la localisation de la route dans l'image). Ce choix est bas e sur un mod ele statistique pour la distribution jointe de l'inconnue et des r eponses aux questions. Le probl eme de minimisation de l'incer-titude (mesur ee par l'entropie de C. Shannon) est formul e de mani ere analytique. Ex ecuter cette strat egie consiste donc a alterner entre …
Effects of weightlifting vs. kettlebell training on vertical jump, strength, and body composition.
(2012)

Effects of weightlifting vs. kettlebell training on vertical jump, strength, and body composition. J Strength Cond Res 26(5): 1199-1202, 2012-The present study compared the effects of 6 weeks of weightlifting plus traditional heavy resistance training exercises vs. kettlebell training on strength, power, and anthropometric measures. Thirty healthy men were randomly assigned to 1 of 2 groups: (a) weightlifting (n = 13; mean ± SD: age, 22.92 ± 1.98 years; body mass, 80.57 ± 12.99 kg; height, 174.56 ± 5.80 cm) or (b) kettlebell (n = 17; mean ± SD: age, 22.76 ± 1.86 years; body mass, 78.99 ± 10.68 kg; height, 176.79 ± 5.08 cm) and trained 2 times a week for 6 weeks. A linear periodization model was used for training; at weeks 1-3 volume was 3 × 6 (kettlebell swings or high pull), 4 × 4 (accelerated swings or power clean), and 4 × 6 (goblet squats or back squats), respectively, and the volume increased during weeks 4-6 to 4 × 6, 6 × 4, and 4 × 6, respectively. Participants were assessed for height (in centimeters), body mass (in kilograms), and body composition (skinfolds). Strength was assessed by the back squat 1 repetition maximum (1RM), whereas power was assessed by the vertical jump and power clean 1RM. The results of this study indicated that short-term weightlifting and kettlebell training were effective in increasing strength and power. However, the gain in strength using weightlifting movements was greater than that during kettlebell training. Neither method of training led to significant changes in any of the anthropometric measures. In conclusion, 6 weeks of weightlifting induced significantly greater improvements in strength compared with kettlebell training. No between-group differences existed for the vertical jump or body composition.
Probabilistic Analysis of Plug-In Electric Vehicles Impact on Electrical Grid Through Homes and Parking Lots
(2013)

Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss.
Multiple-Loop Design Technique for High-Performance Low-Dropout Regulator
(2017)

A new multiple-loop design technique for high-performance low-dropout (LDO) regulator designs has been proposed and successfully implemented in many commercial products for portable smart phone and tablet PC applications. The proposed LDO is composed of five loops that allows designers to obtain a good tradeoff between quiescent current and other performances, such as undershoot, overshoot, and so on. A total of one bandgap reference and 38 LDOs (including n-type and p-type LDOs, which will be named NLDO and PLDO, respectively) were integrated in one power-management IC chip for powering an application processor inside mobile devices. The proposed LDO has been fabricated based on 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu {m}$ </tex-math></inline-formula> CMOS process and supplies various current capacities from 50 to 600 mA; 38 LDOs have been designed and supply different output voltage levels from 0.7 to 3.0 V. One of the proposed NLDOs consumes <inline-formula> <tex-math notation="LaTeX">$14~\mu {A}$ </tex-math></inline-formula> of the quiescent current and features under 56/24 mV of undershoot/overshoot at VOUT =1<italic>V</italic> as the load current steps up from 0 to 300 mA with 300 mA/1 <inline-formula> <tex-math notation="LaTeX">$\mu {s}$ </tex-math></inline-formula> on a 1-<inline-formula> <tex-math notation="LaTeX">$\mu {F}$ </tex-math></inline-formula> output capacitor. The measured output load and line regulations are 1.8 and 0.4 mV, respectively. The measured integrated output noise from 10 Hz to 100 kHz at <inline-formula> <tex-math notation="LaTeX">$\text{I}_{\mathrm{ LOAD}}=10\%$ </tex-math></inline-formula> of maximum current shows 80 <inline-formula> <tex-math notation="LaTeX">$\mu {V}$ </tex-math></inline-formula> rms. The package chip size is <inline-formula> <tex-math notation="LaTeX">$6.25 \times 6.25$ </tex-math></inline-formula> mm<sup>2</sup> with 169 balls.
Non-rigid image registration using self-supervised fully convolutional networks without training data
(2018)

A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different spatial resolutions with deep self-supervision through typical feedforward and backpropagation computation. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.
Identifying HTTPS-Protected Netflix Videos in Real-Time
(2017)

After more than a year of research and development, Netflix recently upgraded their infrastructure to provide HTTPS encryption of video streams in order to protect the privacy of their viewers. Despite this upgrade, we demonstrate that it is possible to accurately identify Netflix videos from passive traffic capture in real-time with very limited hardware requirements. Specifically, we developed a system that can report the Netflix video being delivered by a TCP connection using only the information provided by TCP/IP headers. To support our analysis, we created a fingerprint database comprised of 42,027 Netflix videos. Given this collection of fingerprints, we show that our system can differentiate between videos with greater than 99.99% accuracy. Moreover, when tested against 200 random 20-minute video streams, our system identified 99.5% of the videos with the majority of the identifications occurring less than two and a half minutes into the video stream.
TS-CLOCK: temporal and spatial locality aware buffer replacement algorithm for NAND flash storages
(2014)

NAND flash storage is widely adopted in all classes of computing devices. However, random write performance and lifetime issues remain to be addressed. In this paper, we propose a novel buffer replacement algorithm called TS-CLOCK that effectively resolves the remaining problems. Our experimental results show that TS-CLOCK outperforms state-of-the-art algorithms in terms of performance and lifetime.
Semantic Web User Interfaces : A Systematic Mapping Study and Review
(2012)

Context : Exploration of user interfaces has attracted a considerable attention in Semantic Web research in the last several years. However, the overall impact of these research efforts and the level of the maturity of their results are still unclear. Objective: This study aims at surveying existing research on Semantic Web user interfaces in order to synthesise their results, assess the level of the maturity of the research results, and identify needs for future research. Method : This paper reports the results of a systematic literature review and mapping study on Semantic Web user interfaces published between 2003 and 2011. After collecting a corpus of 87 papers, the collected papers are analysed through four main dimensions: general information such as sources, types and time of publication; covered research topics; quality of evaluations; and finally, used research methods and reported rigour of their use. Results: Based on only the obtained data, the paper provides a high level overview of the works reviewed, a detailed quality assessment of papers involving human subjects and a discussion of the identified gaps in the analysed papers and draws promising venues for future research. Conclusion: We arrive at two main conclusions: important gaps exist which hinder a wide adoption of Semantic Web technologies (e.g. lack user interfaces based on semantic technologies for social and mobile applications) and experimental research hardly used any proven theories and instruments specifically designed for investigating technology acceptance.
System design of the internet of things for residential smart grid
(2016)

The Internet of Things envisions integration, coordination, communication, and collaboration of real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this article studies the design of a large-scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a back-end server, a unified home gateway (UHG) at the end users, and a user interface for mobile devices. We discuss the features of such an IoT system to support a large-scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using XMPP and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.